3,841 research outputs found
One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors
One of the key challenges in applying reinforcement learning to complex
robotic control tasks is the need to gather large amounts of experience in
order to find an effective policy for the task at hand. Model-based
reinforcement learning can achieve good sample efficiency, but requires the
ability to learn a model of the dynamics that is good enough to learn an
effective policy. In this work, we develop a model-based reinforcement learning
algorithm that combines prior knowledge from previous tasks with online
adaptation of the dynamics model. These two ingredients enable highly
sample-efficient learning even in regimes where estimating the true dynamics is
very difficult, since the online model adaptation allows the method to locally
compensate for unmodeled variation in the dynamics. We encode the prior
experience into a neural network dynamics model, adapt it online by
progressively refitting a local linear model of the dynamics, and use model
predictive control to plan under these dynamics. Our experimental results show
that this approach can be used to solve a variety of complex robotic
manipulation tasks in just a single attempt, using prior data from other
manipulation behaviors
Learning Robustness with Bounded Failure: An Iterative MPC Approach
We propose an approach to design a Model Predictive Controller (MPC) for
constrained Linear Time Invariant systems performing an iterative task. The
system is subject to an additive disturbance, and the goal is to learn to
satisfy state and input constraints robustly. Using disturbance measurements
after each iteration, we construct Confidence Support sets, which contain the
true support of the disturbance distribution with a given probability. As more
data is collected, the Confidence Supports converge to the true support of the
disturbance. This enables design of an MPC controller that avoids conservative
estimate of the disturbance support, while simultaneously bounding the
probability of constraint violation. The efficacy of the proposed approach is
then demonstrated with a detailed numerical example.Comment: Added GitHub link to all source code
Non-Linear Model Predictive Control with Adaptive Time-Mesh Refinement
In this paper, we present a novel solution for real-time, Non-Linear Model
Predictive Control (NMPC) exploiting a time-mesh refinement strategy. The
proposed controller formulates the Optimal Control Problem (OCP) in terms of
flat outputs over an adaptive lattice. In common approximated OCP solutions,
the number of discretization points composing the lattice represents a critical
upper bound for real-time applications. The proposed NMPC-based technique
refines the initially uniform time horizon by adding time steps with a sampling
criterion that aims to reduce the discretization error. This enables a higher
accuracy in the initial part of the receding horizon, which is more relevant to
NMPC, while keeping bounded the number of discretization points. By combining
this feature with an efficient Least Square formulation, our solver is also
extremely time-efficient, generating trajectories of multiple seconds within
only a few milliseconds. The performance of the proposed approach has been
validated in a high fidelity simulation environment, by using an UAV platform.
We also released our implementation as open source C++ code.Comment: In: 2018 IEEE International Conference on Simulation, Modeling, and
Programming for Autonomous Robots (SIMPAR 2018
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